Current Mood: 
Blogs I Commented On:
Summary:
This paper focuses on on-line gesture recognition for the domain of Korean Sign Language (KSL) using a glove input device. 14 gestures were included in 25 chosen gestures out of 6,000 in KSL, and each hand postures are recognized by their system using a fuzzy min-max neural network. The authors begin by setting initial gesture positions, which they handle by calibrating data glove data by subtracting subsequent positions by the initial. After selecting 25 glove positions, they determined that they contained 10 basic direction types contaminated by noise from the glove. Since the measured deviation from the real value was within 4 inches, they split the x- and y-axis into 4 regions (for a total of 8) for efficient filtering and computation. The region data at each time unit is stored on 5 cascading registers, and the register values change for current data if it differs from data in the previous time unit. A fuzzy min-max neural network (FMMN) is used to recognize each hand posture, supposedly requires no pre-learning about the posture class, and has on-line adaptability. A fuzzy set hyberbox, which is a 10-dimensional box based on the two flex angles from each finger of the hand, is used in the study. This hyperbox is defined by a min point (V) and a max point (W), corresponds to a membership function, and is normalized between 0 and 1. Initial min-max values (V,W) of the network was created from empirical data of many individuals’ flex angles. A sensitivity parameter is used to regulate the speed of input pattern separation from the hyperbox, where a higher parameter value means a crisper membership function. Thus, when the network receives an input posture, the output is the membership function values for the 14 posture classes, and the input is classified as the posture class with the highest membership value that also exceeds some threshold value. No classification occurs when it falls below the threshold. For given words, the system achieves 85% recognition rates.
Discussion:
This is the second haptics we’ve read which used neural networks for recognition. Unlike the first paper, which used a basic perceptron network, this paper used a more complex fuzzy min-max neural network. It seems to give reasonable recognition rates and claims to have online adaptability. It would have been nice for the authors to have given some insight on why they chose to use FMMN over other types of neural networks, and also provide details on how it did was able to adapt online. I did like how they did provide actual network parameter values to their FMMNs.
1 comments:
It annoys me when authors use very small, hand-chosen data sets and classify them using incredibly complicated neural networks with state of the art algorithms. Especially then when they can only get 85% accuracy. I'd also like to see the rationale behind the choice of network and gesture set.
Post a Comment